Yes and no. Machine learning is a bit like an advanced generalisation of linear regression. And it's successful in a number of "use cases" (that may be hyped up), but the areas where it isn't successful don't get into the news. There are a lot of reasons why machine learning might not end up being the best choice, such as the following:
- the cost of getting the data vs the potential revenue gain from the data: in many cases the ratio between these to isn't known, so it's difficult to build a business case from it. data could be scattered all over the organization, and may not always be in nice "Excel tables" (it could be locked up in images, emails, handwritten log books, etc.)
- not having enough data points
- data quality, the signal to noise ratio in the data
- algorithms become "good" because a tradeoff is being made (the tradeoff makes the algorithm better at one particular problem, but this comes at the expense at poorer performance at other problems). This argument follows the "no free lunch theorem" (which has been mathematically proven) explained here. another example is that if there's already a physics-based model which describes the data, the machine learning model could fail to outperform it.
- extrapolation: regression works best when you interpolate between data points within training data, but starts to perform poorly when you need to extrapolate (make predictions outside the data it was trained on). Likewise, machine learning models tend to perform well within the training data, but if the new data has little resemblance to the original training set, expect the predictive performance to be poor.
So while machine learning is going to be awesome in some specific areas, be also aware that the non-success stories don't get hyped up.